Abstract
Time series algorithms utilize observed past data to make predictions about future values based on their historical patterns and trends. By effectively modeling trends observed in time series data, we can identify and interpret long-term trends, seasonal variations, and recurring patterns. This information is valuable for making informed decisions, predicting future behavior, and identifying opportunities or risks. In this chapter, we will introduce fundamental concepts of time series data, such as stationarity, trend detection, seasonality, and heteroskedasticity. Afterward, we will cover various time series algorithms that will allow readers to analyze and forecast values based on trends in time series data. By the end of this chapter, readers will have a strong foundation in time series algorithms and the tools necessary to analyze and forecast time-dependent data effectively.
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Toe Teoh, T., Jin Goh, Y. (2023). Time Series. In: Artificial Intelligence in Business Management. Machine Learning: Foundations, Methodologies, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-99-4558-0_5
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DOI: https://doi.org/10.1007/978-981-99-4558-0_5
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